We introduce Deformable Animation Ready Templates (DARTs) to address these challenges, which enhance template based garment reconstruction. Our framework employs a robust feature-line regressor network to establish precise deformation constraints guided by input image characteristics. Additionally, we present a novel differentiable Constrained Rigid Deformation Layer (CRDL) that facilitates effective template deformation while preserving the essential geometry of the garment.

We introduce GMOT-Mamba, a novel Mamba-based model prediction framework for Generic Multiple Object Tracking (GMOT) in video sequences. Our approach features a Weighted Feature Pooling (WFP) layer, which processes encoded target states, and an innovative encoder-decoder architecture that leverages Vision-Mamba (ViM) to predict filter weights.

This work introduces SemFaceEdit, a novel method that streamlines the appearance and geometric editing process by generating semantic fields on generative radiance manifolds. Utilizing latent codes, our method effectively disentangles the geometry and appearance associated with different facial semantics within the generated image. In contrast to existing methods that can change the appearance of the entire radiance field, our method enables the precise editing of particular facial semantics while preserving the integrity of other regions.

We propose a hybrid approach that utilizes rigged avatars and the pipeline to generate synthetic datasets to acquire the necessary 3D annotations for training. Our method introduces a simple attention-based MLP network for converting 2D poses to 3D, designed to be independent of the input image to ensure scalability for poses in natural environments. Additionally, we identify that existing anatomical keypoint detectors are insufficient for accurate pose retargeting onto arbitrary avatars.

We present a novel coarser-to-finer approach for deep graphical image inpainting that utilizes GraphFill, a graph neural network-based deep learning framework, and a lightweight generative baseline network. We construct a pyramidal graph for the input-masked image by reducing it into superpixels, each representing a node in the graph. The proposed pyramidal approach facilitates the transfer of global context from coarser to finer pyramid levels, enabling GraphFill to estimate plausible information for unknown node values in the graph

In this work, we present a method for precisely segmenting individual petals within flower images. Petal segmentation is challenging due to its intricate structures and substantial variations among petals across diverse flower species. Moreover, frequent occlusions and the uniformity in appearance and shape of petals within a single flower add complexity to the segmentation process, making it error-prone. We introduce a generalized approach to segment petal instances for any flower species.

We present Deep Mesh Denoising Network (DMD-Net), an end-to-end deep learning framework, for solving the mesh denoising problem. DMD-Net consists of a Graph Convolutional Neural Network in which aggregation is performed in both the primal as well as the dual graph.

In this work, we address the problem of extracting high dimensional, soft semantic feature descriptors for every pixel in an image using a deep learning framework. Existing methods rely on a metric learning objective called multi-class N-pair loss, which requires pairwise comparison of positive examples (same class pixels) to all negative examples (different class pixels). Computing this loss for all possible pixel pairs in an image leads to a high computational bottleneck. We show that this huge computational overhead can be reduced by learning this metric based on superpixels.